Performance-Based Earthquake Engineering 2.0: Machine-Learning and Artificial Intelligence Algorithms for seismic hazard and vulnerability.

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This PhD research project would significantly advance the state-of-the-art revolutionising the field of performance-based earthquake engineering (PBEE), producing new tools that are robust, fast, flexible, and smart. This project falls within the EPSRC Engineering research area, focusing on how artificial intelligence (EPSRC Area1) can be integrated into civil engineering with a specific emphasis on earthquake engineering, structural engineering (EPSRC Area 2) and Ground Engineering (EPSRC Area 3). Context: Globally, 150 earthquakes over magnitude six are experienced yearly, and catastrophic events in the past decade have emphasised the loss of life and economic damage they can produce. Despite the social and economic importance of seismic events, contemporary prediction of earthquake-induced ground motions is still based on empirical models via statistical regression of historical observations with little input from the underlying physics. Several aspects of Earthquake Engineering problems also require capable data-handling/data-mining tools to understand huge volumes of datasets recorded by various ground motions measuring instruments placed across the globe. The field of Machine Learning (ML) has rapidly evolved over the past few years with the promise to modify the role of data science in various disciplines. ML offers advantages in handling complex problems, providing computational efficiency, propagating and treating uncertainties, and facilitating decision-making. With the advancement of ML, its application is not only limited to the field of Artificial Intelligence (AI) but has expanded to several fields of science and engineering. ML has emerged as a promising data-advancement tool to handle vast volumes of datasets created and solve various challenging problems in earthquake engineering. Some applications of machine learning in Earthquake Engineering have already been in practice by several researchers. In contrast, many applications, such as real-time prediction of ground motions and damage detection of earthquakes, have been mainly untapped. Hence, significant opportunities still exist to accelerate their applications in this field of Earthquake Engineering. Therefore, more research on the cross-field of ML and earthquake engineering is needed to embrace the next generation of data sharing and sensor technologies, implement more advanced ML techniques, and develop physics-guided ML models. Henceforth, this research is directed towards understanding the future of implementing ML into Earthquake Engineering problems. Research Objectives & Outcomes: This research aims to unify and harmonise the emerging ML tools with classical Earthquake Engineering approaches to create a modern Performance-Based Earthquake Engineering design and assessment approach. Various ML applications/algorithms will be explored in this research study. Aims and Objectives: Some of the expected outcomes from this study include: Seismic Hazard Analysis. Real-time prediction of ground motion via synthesis of sparse observations and simulations. Seismic fragility assessment. Image Detection to develop next-generation datasets of infrastructure vulnerability. Structural control for earthquake mitigation. Meta-modelling with High-Performance-Computing-based simulation. Identification and Damage Detection. Predicting the movement expected from ground failure during an earthquake.